Etransportation最新文献_第7页

Optimizing the charging behaviors of private BEVs to enhance coordinated charging and V2G in Beijing 优化北京市私人纯电动汽车充电行为，增强充电与V2G的协同

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-12-01 Epub Date: 2025-09-01 DOI: 10.1016/j.etran.2025.100470

Bowen Tian , Wei Shen , Chongyu Zhang , James E. Anderson , Michael W. Degner , Xi Lu , Sheng Zhao , Ye Wu , Shaojun Zhang

Deep electrification of China's transport sector offers CO₂ emission reduction potential but poses reliability challenges to the urban power system. Smart charging strategies for battery electric vehicles (BEVs), including coordinated charging and vehicle-to-grid (V2G), are one of the most promising approaches to realizing the trade-off between decarbonization and stability of the electricity grid. In this study, an integrated model coupling load prediction and unit dispatching was developed to evaluate the multiple impacts of smart charging strategies considering the heterogeneity of individual driving and charging behaviors expected in Beijing in 2030. Compared with previous work, we have further revealed the different impacts of changing drivers' charging preference under uncoordinated charging, coordinated charging and V2G. The lowest operating cost and CO₂ emissions occur in the workplace charging preference (WCP) scenario with uncoordinated charging, but occur in the daily charging (DC) scenario when V2G is applied. It is indicated that smart charging strategies could simultaneously reduce grid operating costs and CO₂ emissions by decreasing the net load (thermal unit power outputs) on the electricity grid. Compared with the uncoordinated charging, using coordinated charging could reduce daily operating cost by 2.67 million RMB and daily CO₂ emissions by 10.23 kt on average, and the adoption of V2G could further increase the reductions to 8.74 million RMB and 24.25 kt CO₂. Annual CO₂ emission reductions enabled by coordinated charging and V2G are estimated to be 3700 kt and 8850 kt, respectively, which are equivalent to 1.2 × and 2.9 × the projected total emissions of the Beijing private BEV fleet. Increases in V2G participation can also smooth the net load profile and improve grid stability. In the DC scenario, the application of V2G reduced the peak net load by almost 30 % compared to the uncoordinated charging. Furthermore, there is a synergy between V2G participation and renewable energy (RE) development. Improving the electricity system and charging technology during future fleet electrification may be facilitated by coordinated charging and V2G opportunities.

中国交通运输行业的深度电气化提供了二氧化碳减排的潜力，但对城市电力系统的可靠性提出了挑战。纯电动汽车（bev）的智能充电策略，包括协调充电和车辆到电网（V2G），是实现脱碳和电网稳定之间权衡的最有前途的方法之一。考虑到2030年北京市居民个人驾驶和充电行为的异质性，建立了负荷预测和单元调度耦合的综合模型，以评估智能充电策略的多重影响。在此基础上，进一步揭示了非协调充电、协调充电和V2G下驾驶员充电偏好变化的不同影响。在不协调充电的工作场所充电偏好（WCP）场景下，运行成本和二氧化碳排放量最低，而在应用V2G的日常充电（DC）场景下，运行成本和二氧化碳排放量最低。研究表明，智能充电策略可以通过降低电网净负荷（热电机组输出功率）来同时降低电网运行成本和二氧化碳排放。与不协调充电方式相比，采用协调充电方式平均可降低运营成本267万元/日，减少二氧化碳排放10.23 kt；采用V2G方式可进一步降低运营成本874万元/日，减少二氧化碳排放24.25 kt。通过协调充电和V2G，预计每年的二氧化碳减排量分别为3700千吨和8850千吨，相当于北京私人纯电动汽车预计总排放量的1.2倍和2.9倍。V2G参与的增加还可以平滑净负载分布并提高电网稳定性。在直流场景中，与不协调充电相比，V2G的应用将峰值净负载降低了近30%。此外，V2G参与与可再生能源发展之间也有协同作用。通过协调充电和V2G机会，可以促进未来车队电气化过程中电力系统和充电技术的改进。

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引用次数: 0

Hierarchical porous transport layers for enhancing mass transport in proton exchange membrane electrolyzer cells 提高质子交换膜电解槽质量传输的分层多孔传输层

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-12-01 Epub Date: 2025-11-05 DOI: 10.1016/j.etran.2025.100510

Jiexin Zou , Yuanbin Sun , Xiuyue Wang , Juntao Chen , Jingke Mo , Siguang Wu , Qiren Chen , Cenkai Zhao , Haijiang Wang , Min Wang

The performance of proton exchange membrane electrolyzer cells (PEMECs) at high current density is constrained by mass transport limitation in conventional porous transport layer (PTL), which is the critical barrier to their large-scale adoption for green hydrogen production. In this paper, a laser-ablated non-penetrating-hole PTL (NP-PTL) with architected pores demonstrates an over 50 % reduction in mass transport overpotential compared to commercial Ti-felt PTL. Through a synergistic combination of in-situ optical diagnostics and two-phase flow modeling, we elucidate the mechanism by which the laser-engineered NP-PTL structure reduces mass transport resistance under high current density operation. Unlike fully perforated designs, the non-penetrating hole architecture maintains optimal contact between the PTL and catalyst layer (CL), minimizing the increase in high-frequency resistance (HFR) and further improving overall electrolyzer efficiency. The NP-PTL not only enhances performance but also exhibits promising initial operational stability, maintaining steady performance during 100-h testing. The laser ablation strategy for fabricating PTL with non-perforated structures offer a novel approach to enhance the performance of PEMECs, thereby accelerating the commercialization of PEMECs.

质子交换膜电解槽（PEMECs）在高电流密度下的性能受到传统多孔输运层（PTL）中质量输运的限制，这是其大规模应用于绿色制氢的关键障碍。在本文中，一种具有结构孔的激光烧蚀非穿透孔PTL （NP-PTL）与商用ti毡PTL相比，其质量输运过电位降低了50%以上。通过原位光学诊断和两相流建模的协同结合，我们阐明了激光工程NP-PTL结构在高电流密度操作下降低质量输运阻力的机制。与全穿孔设计不同，非穿透孔结构保持了PTL和催化剂层（CL）之间的最佳接触，最大限度地减少了高频电阻（HFR）的增加，进一步提高了电解槽的整体效率。NP-PTL不仅提高了性能，而且表现出良好的初始操作稳定性，在100小时的测试中保持稳定的性能。采用激光烧蚀技术制造无孔PTL为提高pemec的性能提供了一种新方法，从而加速了pemec的商业化。

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引用次数: 0

Design parameter optimization for sulfide-based all-solid-state batteries with high energy density 高能量密度硫化物基全固态电池设计参数优化

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-12-01 Epub Date: 2025-10-31 DOI: 10.1016/j.etran.2025.100507

Jiyoung Kim , Charles Mish , Alexandre T.R. Guibert , Filippo Agnelli , Marta Vicencio , So-Yeon Ham , Min-Sang Song , Ying Shirley Meng , Jeong Beom Lee , H. Alicia Kim

Sulfide-based all-solid-state batteries (ASSBs) are promising candidates for applications requiring high energy density and enhanced safety, with the potential to replace conventional Li-ion batteries. Despite significant advances in material design and engineering, the impact of material properties and process variables on cell energy density remains poorly understood. In this study, we employed a validated pseudo-two-dimensional (P2D) model to investigate how volumetric and gravimetric energy densities of ASSBs change as function of various cell design parameters and to perform mathematical optimization to maximize energy densities. Model parameters were derived from pellet cell experiments, incorporating a cathode composite with high-capacity NCM811 and densely packed fine argyrodite, alongside a bulk solid electrolyte separator with high ionic conductivity. The model's accuracy was confirmed by comparing simulation results with experimental voltage profiles, resulting in a root mean square error of 0.028 mV and an energy discrepancy of 0.7 %. Using the validated P2D model, we set energy densities as objective functions and scaled the pellet cell structure to automotive pouch cell dimensions to assess practical energy densities. A comprehensive sensitivity study was conducted on design parameters within the solid electrolyte separator and cathode composite. The weight percentage of the cathode active material was identified as a highly sensitive parameter, with other cathode composite parameters showing strong dependence on it. Employing a gradient-free direct search optimization method, we identified optimal design parameters that improved the volumetric and gravimetric energy densities by 62.5 % and 66.3 %, respectively, relative to reference values based on experimental parameters for a single cell.

硫化物基全固态电池（assb）在高能量密度和安全性要求较高的应用中具有很好的前景，有可能取代传统的锂离子电池。尽管在材料设计和工程方面取得了重大进展，但材料特性和工艺变量对电池能量密度的影响仍然知之甚少。在这项研究中，我们采用了一个经过验证的伪二维（P2D）模型来研究assb的体积和重量能量密度随不同细胞设计参数的变化，并进行数学优化以最大化能量密度。模型参数来源于颗粒电池实验，包括高容量NCM811和密集堆积的细银柱石阴极复合材料，以及具有高离子电导率的大块固体电解质分离器。通过仿真结果与实验电压曲线的比较，验证了该模型的准确性，得到的均方根误差为0.028 mV，能量差为0.7%。利用验证的P2D模型，我们将能量密度设置为目标函数，并将颗粒电池结构缩放到汽车袋状电池尺寸，以评估实际能量密度。对固体电解质分离器和阴极复合材料的设计参数进行了综合敏感性研究。阴极活性物质的重量百分比是一个高度敏感的参数，其他阴极复合材料参数对其有很强的依赖性。采用无梯度直接搜索优化方法，我们确定了最优设计参数，相对于基于单个细胞实验参数的参考值，体积和重量能量密度分别提高了62.5%和66.3%。

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引用次数: 0

Demystifying data-driven approaches for battery electric transportation: Challenges and future directions 揭开电池电动交通数据驱动方法的神秘面纱：挑战和未来方向

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-12-01 Epub Date: 2025-10-13 DOI: 10.1016/j.etran.2025.100501

Boryann Liaw , Weihan Li , Luc Raijmakers , Lisen Yan , Haider Adel Ali Ali , Anna Windmüller , Chih-Long Tsai , Dirk Uwe Sauer , Rüdiger-A. Eichel

Data-driven techniques leveraging artificial intelligence (AI) and machine learning (ML) are growing as favorable approaches to overcome challenges in predicting complicated behaviors of battery systems. Yet the data-driven approaches continue to face stiff challenges, including the difficulties in acquiring exhausting resources for data acquisition, managing escalating data quality issues to build robust data-driven capability, and sharing multimodal data from a variety of sources using wide ranges of test and operating conditions, and the lack of a reliable framework to verify and validate data consistency so the accuracy of the heuristic data reductions could be assessed. These challenges undermine the reach of a cost-effective and robust approach to predict battery performance and life with high fidelity for battery management. Here, we look into the root of these challenges and provide exemplified guidance to shed light on future directions, aiming for addressing these issues effectively.

利用人工智能（AI）和机器学习（ML）的数据驱动技术正在成为克服预测电池系统复杂行为挑战的有利方法。然而，数据驱动的方法仍然面临严峻的挑战，包括难以获取用于数据采集的耗尽资源，管理不断升级的数据质量问题以建立强大的数据驱动能力，以及使用广泛的测试和操作条件共享来自各种来源的多模式数据，以及缺乏可靠的框架来验证和验证数据一致性，因此可以评估启发式数据约简的准确性。这些挑战削弱了预测电池性能和寿命的高保真度方法的成本效益和可靠性。在这里，我们将探讨这些挑战的根源，并提供范例指导，以阐明未来的方向，旨在有效地解决这些问题。

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引用次数: 0

Maritime electrification pathways for sustainable shipping: Technological advances, environmental drivers, challenges, and prospects 可持续航运的海上电气化途径：技术进步、环境驱动因素、挑战和前景

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-12-01 Epub Date: 2025-09-01 DOI: 10.1016/j.etran.2025.100462

Zhe Wang , Pengzhi Liao , Fei Long , Zhengquan Wang , Yulong Ji , Fenghui Han

Maritime electrification has gained unprecedented momentum as the shipping industry faces stringent global decarbonization targets and increasingly rigorous International Maritime Organization (IMO) regulations. This review provides a systematic assessment of the technological advances, environmental drivers, challenges, and future prospects of ship electrification, with a focus on three primary pathways: Battery-Electric Ships (BES), Hybrid-Electric Ships (HES), and Fuel Cell Electric Ships (FCES). The analysis encompasses technological maturity levels (TRL), economic competitiveness, lifecycle environmental performance, and regional deployment feasibility. Findings indicate that HES currently dominate commercial applications owing to their operational flexibility and compliance readiness, while BES demonstrate strong potential for short-sea and inland shipping routes, and FCES represent a long-term solution for deep decarbonization provided that green hydrogen and ammonia infrastructure becomes available. The review highlights persistent barriers, including limited energy density for large vessels, insufficient megawatt-scale charging and refueling infrastructure, durability and reliability concerns under harsh marine conditions, and misaligned global policy frameworks. Notable contributions include the provision of quantitative TRL evaluations for BES, HES, and FCES, a comparative analysis of regional deployment strategies targeting emission-intensive maritime zones, and the identification of AI-enabled digital twin technologies as a promising approach to optimize energy management and fleet operations. To accelerate maritime electrification, future research is directed toward breakthroughs in solid-state batteries, advanced corrosion-resistant materials, safe and efficient hydrogen/ammonia storage, port-level renewable microgrids, and standardized international safety regulations. Overall, this review establishes a comprehensive roadmap for academia, industry stakeholders, and policymakers to advance the transition toward sustainable, zero-emission shipping.

随着航运业面临严格的全球脱碳目标和国际海事组织（IMO）日益严格的规定，海上电气化获得了前所未有的动力。本文对船舶电气化的技术进步、环境驱动因素、挑战和未来前景进行了系统评估，重点介绍了三种主要途径：电池电动船舶（BES）、混合动力电动船舶（HES）和燃料电池电动船舶（FCES）。分析包括技术成熟度水平（TRL）、经济竞争力、生命周期环境绩效和区域部署可行性。研究结果表明，由于其操作灵活性和合规性，HES目前在商业应用中占主导地位，而BES在短途和内陆航线上显示出强大的潜力，而FCES则代表了深度脱碳的长期解决方案，前提是绿色氢和氨基础设施可用。该评估强调了持续存在的障碍，包括大型船舶的能量密度有限，兆瓦级充电和加油基础设施不足，恶劣海洋条件下的耐久性和可靠性问题，以及不一致的全球政策框架。值得注意的贡献包括为BES、HES和FCES提供定量TRL评估，对针对排放密集型海域的区域部署策略进行比较分析，以及将人工智能支持的数字孪生技术确定为优化能源管理和船队运营的有前途的方法。为了加速海上电气化，未来的研究方向是在固态电池、先进的耐腐蚀材料、安全高效的氢/氨储存、港口级可再生微电网以及标准化的国际安全法规方面取得突破。总体而言，本综述为学术界、行业利益相关者和政策制定者制定了全面的路线图，以推进向可持续、零排放航运的过渡。

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引用次数: 0

3D-printed honeycomb lithium-silicon alloy anodes for stabilized interface in sulfide all-solid-state batteries 用于硫化物全固态电池稳定界面的3d打印蜂窝锂硅合金阳极

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-12-01 Epub Date: 2025-09-05 DOI: 10.1016/j.etran.2025.100476

Lutong Wang , Ziqi Zhang , Fuqiang Xu , Jixian Luo , Chuang Yi , Hong Li , Liquan Chen , Fan Wu

Solid-state batteries have emerged as a crucial development direction for next-generation energy storage technologies, owing to their high energy density, long cycle life, and excellent safety. However, the most challenging issue of interfacial contact/degradation in solid-state batteries remains unsolved. Herein, a novel Si-C interlocking honeycomb electrode is designed/realized via 3D printing technology. Achieves 98.9 % capacity retention over 2100 cycles at 1C. The honeycomb pore walls form a mortise-tenon structure with the electrolyte to maintain good interfacial contact, while the hard carbon layer isolates the electrolyte from the lithium-silicon interface, thereby stabilizing the growth of the solid electrolyte interphase (SEI) and achieving stress-electrochemical coupling regulation. Moreover, as the honeycomb channels form an interpenetrating structure with the solid electrolyte, a three-dimensional ion transport network is established, shortening the lithium-ion diffusion path, enhancing the interfacial contact between the electrode and solid electrolyte, reducing the risk of lithium dendrite formation, and improving the rate performance of all-solid-state batteries. This approach leverages structural design to enhance material performance, for the first time enabling the compatibility of 3D-printed structured silicon-based anodes with sulfide-based all-solid-state systems, thus providing a scalable solution for next-generation high-energy-density batteries.

固态电池具有能量密度高、循环寿命长、安全性好等优点，已成为下一代储能技术的重要发展方向。然而，固态电池中最具挑战性的界面接触/降解问题仍未得到解决。本文采用3D打印技术设计/实现了一种新型硅碳联锁蜂窝电极。达到98.9%的容量保持超过2100循环在1C。蜂窝孔壁与电解质形成榫卯结构，保持良好的界面接触，而硬碳层将电解质与锂硅界面隔离，从而稳定固体电解质界面相（SEI）的生长，实现应力-电化学耦合调节。此外，由于蜂窝通道与固体电解质形成互穿结构，建立了三维离子输运网络，缩短了锂离子扩散路径，增强了电极与固体电解质的界面接触，降低了锂枝晶形成的风险，提高了全固态电池的速率性能。这种方法利用结构设计来提高材料性能，首次实现了3d打印结构硅基阳极与硫化物基全固态系统的兼容性，从而为下一代高能量密度电池提供了可扩展的解决方案。

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引用次数: 0

Online generation of full-frequency electrochemical impedance spectra for Lithium-ion batteries using early-stage partial relaxation voltage curve 利用早期部分弛豫电压曲线在线生成锂离子电池全频率电化学阻抗谱

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-12-01 Epub Date: 2025-09-13 DOI: 10.1016/j.etran.2025.100482

Jiajun Zhu , Xin Lai , Zhicheng Zhu , Penghui Ke , Yuejiu Zheng , Xiaopeng Tang , Xiang Li , Ye Yuan , Haoyu Chong , Chenhui Yan , Ying Wang , Yanke Lin , Xiaolei Zhou , Yingjie Chen

Electrochemical impedance spectroscopy (EIS) serves as a powerful non-destructive tool for lithium-ion battery state assessment, yet its real-time application faces significant challenges including expensive hardware requirements, time-consuming measurements, and stringent data quality demands. This study develops a hardware-free online electrochemical impedance spectroscopy using only relaxation voltage, achieved through a physics-informed neural network (PINN) that predicts full-frequency EIS from early-stage partial relaxation curves. The proposed approach exhibits remarkable insensitivity to battery state of charge and state of health, as validated by a comprehensive dataset containing over 300 impedance spectra from four batteries under various aging conditions. Experimental results demonstrate accurate EIS prediction with relative errors (RE) below 5.6 % and mean absolute errors (MAE) below 1.12 mΩ when using complete relaxation curves. Crucially, the method maintains reliability under practical constraints, achieving maximum RE of 6.1 % and MAE of 1.29 mΩ even with limited sampling data and shortened relaxation curves. By enabling online full-frequency EIS acquisition through relaxation voltage signals without hardware requirements, this work establishes a new paradigm for real-time battery diagnostics, providing valuable insights for state estimation and fault detection in battery management systems.

电化学阻抗谱（EIS）是锂离子电池状态评估的一种强大的非破坏性工具，但其实时应用面临着巨大的挑战，包括昂贵的硬件要求、耗时的测量和严格的数据质量要求。本研究开发了一种仅使用弛豫电压的无硬件在线电化学阻抗谱，通过物理信息神经网络（PINN）实现，该网络可以从早期部分弛豫曲线预测全频率EIS。通过包含4个电池在不同老化条件下的300多个阻抗谱的综合数据集验证了该方法对电池充电状态和健康状态的不敏感性。实验结果表明，使用完全松弛曲线预测EIS的相对误差（RE）小于5.6%，平均绝对误差（MAE）小于1.12 mΩ。至关重要的是，该方法在实际约束下保持了可靠性，即使在有限的采样数据和缩短的松弛曲线下，也实现了最大的6.1%的RE和1.29 mΩ的MAE。通过在没有硬件要求的情况下通过松弛电压信号实现在线全频率EIS采集，这项工作为实时电池诊断建立了一个新的范例，为电池管理系统的状态估计和故障检测提供了有价值的见解。

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引用次数: 0

The role of resource sustainability for lithium-ion batteries -A review of existing carbon emission reduction perspectives 锂离子电池资源可持续性的作用——现有碳减排观点综述

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-12-01 Epub Date: 2025-09-29 DOI: 10.1016/j.etran.2025.100491

Wenfang Gao , Xianju Zeng , Weiguang Lv , Zhengqing Ye , Bingxin Zhou , Guangming Zhang , Zhijun Ren , Zhiyuan Feng , Wei Jin , Zhi Sun

The resource recycling of lithium-ion batteries (LIBs) can significantly reduce the carbon emission, which has received unprecedented attention from both the academic and industrial communities. However, the large consumption of valuable materials (e.g., Li, Co, Ni, Mn, graphite) for LIBs not only intensifies the pressure on global resource supply, but also raises the carbon footprint of the industry. Herein, this review systematically analyses the LIBs industry from the aspects of resource supply and resource cycle, in combination with the carbon emission reduction analyzation of LIBs industry. By analyzing the development status of LIBs materials, the resource composition and critical metals are clearly clarified. With LIBs demand increasing, the resource criticality, primary and secondary resource supply are deeply evaluated where Li and Co supply face enormous challenges. The recycling of spent LIBs gives an effective way for resource utilization and circulation. The carbon emission intensity of the whole LIBs industrial chain is discussed from the perspective of resource supply, utilization, and balance, where the carbon emission reduction mainly relies on the use of low-carbon energy and the recycling/reproduction processes. This critical review revealed that resource sustainability and carbon neutralization are an inseparable system, and can give guidance to the development of LIBs materials to ensure the sustainable development of resources in the future.

锂离子电池（LIBs）的资源回收利用可以显著降低碳排放，受到了学术界和工业界前所未有的关注。然而，锂离子电池大量消耗有价值的材料（如Li， Co, Ni, Mn，石墨）不仅加剧了全球资源供应的压力，而且还增加了该行业的碳足迹。本文结合碳减排分析，从资源供给和资源循环两个方面对锂离子电池产业进行了系统分析。通过分析lib材料的发展现状，明确了lib材料的资源组成和关键金属。随着锂离子电池需求的增加，对锂和钴供应面临巨大挑战的资源临界性、一次和二次资源供应进行了深入评估。废lib的回收利用为资源利用和循环利用提供了有效途径。从资源供给、利用和平衡的角度探讨整个lib产业链的碳排放强度，其中碳减排主要依靠低碳能源的使用和循环/再生产过程。这一批判性综述揭示了资源可持续性与碳中和是一个不可分割的系统，可以为lib材料的发展提供指导，以确保未来资源的可持续发展。

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引用次数: 0

Physics-enhanced U-net and deep reinforcement learning for automated optimization of pin-fin heat sinks in electric vehicle power modules 基于物理增强U-net和深度强化学习的电动汽车电源模块插片散热器自动优化

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-12-01 Epub Date: 2025-09-03 DOI: 10.1016/j.etran.2025.100463

Yubo Lian, Heping Ling, Gan Song, Jiapei Yang, Hanzhi Wang, Zhe Zhang, Shaokuan Mao, Bin He

The use of pin-fin structures in compact energy devices, such as electric vehicle power modules, is a widely adopted thermal management strategy to enhance heat transfer efficiency. In this study, we present an innovative deep learning framework that integrates a physics-enhanced U-net architecture with a deep reinforcement learning agent to achieve autonomous optimal design of pin-fin arrays. The physics-enhanced U-net is trained to predict thermal-flow fields, while the integrated deep reinforcement learning agent autonomously optimizes pin-fin configurations to minimize both pressure drop and junction temperature. First, we generate a high-fidelity training dataset through an automated computational pipeline that integrates COMSOL Multiphysics for thermal-flow field simulations with a custom Matlab script for parametric generation of 1080 training samples. Subsequently, we train our physics-enhanced U-net architecture to predict the velocity, pressure and temperature fields from various pin-fin structure inputs. The proposed model demonstrates both high prediction accuracy and robustness, achieving mean-squared-errors on the order of 10⁻⁴ for all output fields. As a result, the trained U-net model achieves exceptional prediction accuracy, demonstrating 93.9 % precision for pressure drop and 99.5 % for junction temperature. Finally, we integrate the deep reinforcement learning agent with the trained U-net model to establish an automated optimization framework for pin-fin design, enabling intelligent exploration of design space. The proposed deep learning framework successfully automates the optimization of pin-fin heat sinks for a high power density module. The model demonstrates exceptional capability in generating optimal designs, with the optimized configuration achieving an 8.8 K reduction in junction temperature and 11.3 % decrease in pressure drop comparing to a baseline design. These improvements can be translated into approximately 10 % augmentation in power output, which validates both the effectiveness and robustness of our deep learning driven design approach.

在电动汽车电源模块等紧凑型能源器件中，采用针翅结构是一种广泛采用的热管理策略，以提高传热效率。在本研究中，我们提出了一个创新的深度学习框架，该框架将物理增强的U-net架构与深度强化学习代理集成在一起，以实现引脚鳍阵列的自主优化设计。物理增强的U-net经过训练，可以预测热流场，而集成的深度强化学习代理可以自主优化引脚鳍配置，以最小化压降和结温。首先，我们通过自动化计算管道生成高保真度的训练数据集，该管道集成了COMSOL Multiphysics用于热流场模拟，以及用于参数化生成1080个训练样本的自定义Matlab脚本。随后，我们训练了物理增强的U-net架构，以预测来自不同鳍片结构输入的速度、压力和温度场。该模型具有较高的预测精度和鲁棒性，所有输出字段的均方误差均在10−4量级。结果，训练后的U-net模型达到了优异的预测精度，对压降的预测精度为93.9%，对结温的预测精度为99.5%。最后，我们将深度强化学习智能体与训练好的U-net模型相结合，建立了鳍片设计的自动化优化框架，实现了设计空间的智能探索。提出的深度学习框架成功地自动优化了高功率密度模块的鳍片散热器。该模型在生成优化设计方面表现出卓越的能力，与基线设计相比，优化后的配置实现了结温降低8.8 K，压降降低11.3%。这些改进可以转化为大约10%的功率输出增加，这验证了我们深度学习驱动设计方法的有效性和鲁棒性。

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引用次数: 0

A reconfigurable battery system for a Tesla Model Y: Package and efficiency analysis 特斯拉Y型可重构电池系统：封装与效率分析

IF 17 1区工程技术 Q1 ENERGY & FUELS

Etransportation

Pub Date : 2025-12-01 Epub Date: 2025-09-08 DOI: 10.1016/j.etran.2025.100464

Andreas Wiedenmann , Julian Estaller , Johannes Buberger , Wolfgang Grupp , Manuel Kuder , Antje Neve , Thomas Weyh

This study investigates the integration of a modular multilevel inverter-based reconfigurable battery system into an existing electric vehicle. The aim is to evaluate how such systems can replace conventional traction inverters, battery management systems, and on-board chargers. To this end, a classification of the different topology levels and possible forms of integration of power electronics, control logic, and driver electronics is performed. A Tesla Model Y’s traction battery is redesigned, retaining its structural properties and the 4680 cell format. A package analysis shows that the multilevel system occupies a volume comparable to the conventional battery pack, while the volume previously reserved for dedicated power electronics becomes available. Efficiency simulations demonstrate that the multilevel inverter can increase the overall vehicle efficiency, especially in situations with low driving speeds and high torque requirements. As a result, WLTP energy consumption is reduced from 14.9 kWh/100km to 14.5 kWh/100km. However, the battery efficiency is reduced at higher speeds due to higher cell currents. In addition, the system enables bidirectional charging at full system power, including supply to external loads or the grid, and a more integrated vehicle architecture.

本研究探讨了一种基于多电平逆变器的模块化可重构电池系统与现有电动汽车的集成。目的是评估这种系统如何取代传统的牵引逆变器、电池管理系统和车载充电器。为此，对电力电子器件、控制逻辑和驱动电子器件的不同拓扑级别和可能的集成形式进行了分类。特斯拉Y型的牵引电池进行了重新设计，保留了其结构特性和4680电池格式。封装分析表明，多电平系统占用的体积与传统电池组相当，而以前为专用电力电子设备保留的体积变得可用。效率仿真结果表明，多电平逆变器可以提高整车效率，特别是在低速和高转矩要求的情况下。因此，WLTP能耗从14.9千瓦时/100公里降低到14.5千瓦时/100公里。然而，由于更高的电池电流，电池效率在更高的速度下会降低。此外，该系统还可以在全系统功率下进行双向充电，包括向外部负载或电网供电，以及更集成的车辆架构。

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引用次数: 0